The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

Magnetic resonance imaging (MRI) offers visual representations of the interior of a body for clinical analysis and medical intervention. The MRI process is subjected to a variety of image processing and machine learning approaches to identify, diagnose, and classify brain diseases as well as detect abnormalities. In this paper, we propose an improved classification method for distinguishing cancerous and noncancerous tumors from brain MRI images by using Log Polar Transformation (LPT) and convolutional neural networks (CNN). The LPT has been applied for feature extraction of rotation and scaling of distorted images, while the integration of CNN introduces a machine learning approach for the tumor classification of distorted images. The dataset was formed with images of seven different brain diseases, and the training set was formed by applying CNN with the extracted features. The proposed method is then evaluated in comparison to state-of-the-art algorithms, showing a definite improvement of the former. The obtained results show that the machine learning approach offers better classification with a success rate of about 96% in both plain brain MR images and rotation- and scale-invariant brain MR images. This work also successfully classified T-1 and T-2 weighted images of neoplastic and degenerative brain diseases. The obtained accuracy is perfected by several kernel procedures, while the combined performance of the two wavelet transformations and a strong dataset make our method robust and efficient. Since no earlier study on machine learning approaches with rotated and scaled brain MRI has come to our attention, it is expected that our proposed method introduces a new paradigm in this research field.

Purpose: This paper aims to discuss the factors that influence tourists' travel intentions and how these intentions may be altered when visiting a specific destination. This study aims to ascertain the significance of understanding the evolving critical factors that boost tourists' intention to travel. Notably, it explores factors influencing changing tourist intentions to visit Asian countries. These factors are the digital literacy of macro business owners, marketing ability, niche signature product and facility of tourist healthcare.Design/methodology/approach: The study was conducted using a quantitative research approach. The researcher gathered data from selected Asian countries, including Thailand, Indonesia, India, Singapore and Brunei. The researcher constructed the self-administered questionnaires using Google Docs, WhatsApp and email to collect the data. Statistical tools structural equation modelling and partial least squares (SEM-PLS) were utilised to analyse the data and understand its reliability and validity.Findings: The intention to engage in tourist travel is a highly influential indicator and predictor of prospective visitors. The examination of the social role of tourists reveals that the conduct exhibited by tourists can serve as a potential indicator of the conduct displayed by other individuals. Tourists play a significant role in shaping the digital literacy of macro-business owners and the quality of healthcare facilities available to tourists in the tourism industry. Small business owners also pursue these aims through various marketing strategies and distinctive products or services, including catering to the needs of tourists and attracting potential customers who have not yet experienced travel. Tourist travel pertains to tourist behaviour in acquiring and relinquishing tourism services.Research limitations/implications: It focuses on infrastructural development, cultural offerings and sustainability practices to enhance the attractiveness of Asian countries. Policymakers should consider changes to tourism policies, such as visa restrictions and safety protocols, to align with evolving travel goals. The study can improve the overall tourist experience in Asian countries by investing in cultural events and digital technologies. It can also help in crisis management strategies and recovery processes. Stakeholders should formulate policies and regulations that maximise economic benefits while promoting environmental practices. The goal is to enhance visitor influx and market unique attractions, advance sustainable tourism principles and contribute to academic literature. The study’s outcomes will have practical applications, helpful advice, benefits for the tourism industry and stakeholder engagement.Originality/value: This study emphasises the significance of understanding tourists' travel intentions while choosing their location. Hence, this study is deemed valuable for future research endeavours, particularly in elucidating the purposes and roles of tourists' behaviour that contribute to identifying factors influencing destination selection.